This invention relates to the field of image processing. More specifically, the invention relates to the generation of a rolled surface image representation from a sequence of partial surface images.
In an image processing system, the first step is to image the object of interest. Often a single view of the object is not sufficient for recognition purposes. Instead, a suitably distributed image sequence comprising of different views of the object can be used to represent all the views of the object. In order to combine this sequence into a single image, special mosaicking techniques are used.
One application in which such mosaicking would be desirable is fingerprint imaging. Fingerprints have been used for automatic authentication and identification purposes for several decades. The following journal article describes examples of the state of the prior art. This reference is incorporated herein by reference in its entirety.
N. K. Ratha, S. Chen and A. K Jain, Adaptive flow orientation based feature extraction in fingerprint images, Pattern Recognition, vol. 28, no. 11, pp. 1657-1672, Nov. 1995.
As shown in FIG. 1, a typical automatic fingerprint identification systems consists of the above mentioned image acquisition stage (110) followed by feature extraction (120) and feature matching (130). The system can be either entirely local, with the fingerprint being processed on the client, or distributed, with the image or feature list sent over a network to some remote server. In either implementation, the first step (110) is to acquire a fingerprint image such as shown in FIG. 2 (item 250). There are several techniques available for sensing the fingerprint image. These include optical, capacitance, thermal and ultrasound. A typical inkless fingerprint scanner uses a prism and total frustrated internal reflection techniques to image the finger touching the prism surface. (For example see U.S. Pat. No. 3,200,701 to W. White and U.S. Pat. No. 5,467,403 to Fishbine et al.) A newer generation of inkless scanners use electrostatic or capacitive coupling techniques to sense the ridges and valleys of the finger (cf. U.S. Pat. No. 5,852,670 to Setlak et al.).
The step (120) in a fingerprint analysis system is to extract tightly localized minutia features. FIG. 2 shows such features, like the ending 203 of ridge 205 or the bifurcation 201 of a ridge. These features can be represented by a list 210 which indicates the type of each minutia and its location coordinates within the image. Other information such as the local ridge orientation or a detection confidence measure could also be added to each list element. These features are then used in final step (130) for matching the extracted target minutiae set to some stored reference minutiae set.
During image acquisition (110) in a fingerprint analysis system, the images acquired are typically of one of two types. There are xe2x80x9crolledxe2x80x9d fingerprint images, which are images of a finger from nail-to-nail and xe2x80x9cdabxe2x80x9d or plain fingerprint images, which are images of a finger as it touches the fingerprint image acquisition surface. The fingerprint dabs are good enough for verification or identification purposes. However, the rolled fingerprints have more information for matching to images of any part of the finger, and hence are preferred for the enrollment processxe2x80x94registering a person""s identity and storing an associated unique fingerprint template.
In the traditional method of acquiring a fingerprint using ink and paper, the rolled impressions are available effortlessly through the simple process of just rolling the finger. When using an ink-less scanner for acquiring digital images directly, obtaining a rolled digital fingerprint image is not as straightforward. These scanners are set up to quickly snap single images, and do not accumulate pressure-based markings across their surface as with the inked finger rolling on paper. Digital image scanners are preferable in most applications because they are faster and less messy. Yet rolled prints contain the greatest amount of information.
Thus a method that allowed a live scan fingerprint reader to generate the equivalent of rolled impressions would be desirable. An ink-less scanner with a fast enough image acquisition system can grab multiple partial surface images while a subject rolls a finger over the surface of the scanner. Each partial surface image just shows that portion of the finger""s surface which is currently in contact with the scanner. Appropriate mosaicking software would allow the integration of such a multitude of partial surface images into a single, more detailed composite image of the finger surface.
The construction of a rolled impression from a sequence of partial fingerprint images can be viewed as an image mosaicking technique. That is, it consists of registering and combining a sequence of images with a small field of view to generate an image having a larger field of view and possibly higher resolution. Most of the prior art in this area of image mosaicking is quite compute intensive. A review of the techniques in this area is available in the paper cited below. This reference is incorporated herein by reference in its entirety.
N. K Ratha, J. H. Connell and R. M. Bolle, Image Mosacing for Rolled Fingerprint Construction International Conf. On Pattern Recognition, Brisbane, Australia, 1998, pp. 1651-1653.
The following references give further background on the reconstruction specifically of rolled fingerprints using software or hardware techniques and are also incorporated by reference in their entirety:
B. H. Fishbine, G. M. Fishbine, T. D. Klein and D. E. Germann, System for Generating Rolled Fingerprint Images U.S. Pat. No. 4,933,976, June 1990.
W. J. Chilcott, Full Roll Fingerprint Apparatus U.S. Pat. No. 4,946,276, August 1990.
E. Ranalli and B. G. Broome, Fingerprint Imaging U.S. Pat. No. 5,625,448, April, 1997
Fishbine""s system uses multiple dab impressions to reconstruct a large area gray-scale fingerprint image equivalent to a rolled ink image. However, it requires the processor to compute pixel variances to determine the xe2x80x9cactive areaxe2x80x9d of each successive image (that is, the portion containing the fingerprint). Only pixels from these regions in each image are used to create the composite result. Moreover, this system only really merges pixel values in a slim transition region at the trailing edge of the active area of each new impression Locating this region requires additional computation to determine the nearest sufficiently dark area within the previously defined active area. The total amount of computation required in this approach makes it undesirable for some applications. Also, the quality of the result can be adversely affected if the determination of the impression""s active area or trailing edge is inaccurate.
Chilcott""s system uses a linear sensor and scanning optics to build up an image one line at a time. A series of lights is turned on and off to demarcate the line along which the optical scan is currently being taken. To develop a fill rolled print the subject rolls his finger on the glass platen at the same rate as the optical scan as indicated by the lights. Alternately, a motor can rotate the finger or the camera.
Ranalli""s system is similar except it uses a flat glass scanning surface instead of a curved one. While rolled image construction is not described explicitly in either patent, the situation here is fairly straightforward since each scan line is distinct. There is no need to merge information from different scans since the linear images they produce never overlap. Instead, the individual scans can just be concatenated to form the final image.
One disadvantage of these systems is that the subject must carefully roll his finger at a certain rate or else the current scan line will miss his finger. A second disadvantage is that each portion of the finger is only imaged once. This precludes using multiple observations as a basis for generating a confidence measure for extracted features, and is eliminates the possibility of producing a more accurate image of the finger by carefully merging overlapped regions.
An object of this invention is an improved system and method for constructing a rolled surface image from a sequence or set of partial surface images.
An object of this invention is an improved system and method for constructing a rolled surface image from a sequence or set of partial surface images, independent of roll rates.
An object of this invention is an improved system and method for computing a more complete surface feature set from a sequence or set of partial surface images.
A further object of this invention is an improved system for computing a confidence measure for each feature in a feature set derived from a sequence or set of partial surface images.
The invention is a system and method for constructing a rolled surface representation from a sequence of partial surface images. While the invention describes specific issues concerned with fingerprints as the objects to be imaged, it also applies to other objects with physically textured convex surfaces.
In a present preferred embodiment, the rolled fingerprint image is constructed from a sequence of partial images (dab impressions). Bach dab impression is a partial image of the finger (object) surface. A sequence of individual dab images is acquired as the finger is progressively rolled across the sensing surface. The quality of the partial images is independent of roll rate because only parts of the object that are within a image depth from the imaging device are taken in each of the partial images. In a preferred embodiment, this image depth is determined by taking an image of the object only at the point the surface normal of the object is parallel to an imaging axis, within a tolerance.
To produce a rolled fingerprint output image, i.e., a composite image, the value of each pixel of the composite image is derived by performing a suitable pixel operation (combination) on the series of counterpart (corresponding) pixels in the input dab images This is possible because each partial image is represented as a fixed size and each pixel in each of the partial images has a unique position in the partial image, called the image position.
In an alternative embodiment, to produce a complete minutiae set, minutiae features are extracted from each individual dab image and then matched across dab images to generate clusters of observations associated with each output minutiae. These clusters are then analyzed to produce more accurate positional information as well as a confidence measure for each observed minutiae feature in the complete set covering the rolled surface of the finger.